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There is no dependable single tell that proves content was made by AI or by a person. Start with its source and history, then check for supported provenance signals and corroborate the content independently. A detector result can be evidence, but neither a positive nor a negative result settles who created something.
What can—and can’t—show that content came from AI?
“AI-generated” and “human-made” are not always opposites. A person may use AI to draft, translate, edit, or produce only part of a work. A signal may show that a supported system generated or processed some content; it does not measure how much a person contributed.
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Keep these questions separate: where content came from, who authored or owns it, whether it is accurate, and whether a disclosure is required. A provenance result does not answer all of them.
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For an image or audio file, look for an official verification tool that supports the file type and provenance signal. OpenAI’s checker, for example, looks for supported signals associated with OpenAI—not every AI provider. OpenAI recommends checking the original exported file; cropping or converting an image can interfere with a check. For its audio tool, clips between 10 and 60 seconds generally produce the best results. OpenAI’s guidance on provenance signals explains the scope and limitations.
#1 Best Overall
A detected signal is evidence that a supported provenance marker was found. It does not show that the file is accurate, untouched, or wholly AI-generated. Likewise, no signal does not establish human authorship: the content may predate a rollout, come from an unsupported model or format, have lost metadata, or have been edited in a way that degraded a watermark.
Text checks have narrower availability and scope
For text, first establish whether the relevant provider marked output from that model, region, and time period, and whether an authorized detector is available to you. OpenAI’s October 5, 2026 announcement says access to its text detector initially requires approval for researchers and expert organizations. It describes a rollout for eligible ChatGPT and Codex output in the EU, plus opt-in API watermarking for select models. Availability may change; these arrangements are not a universal text-detection service. OpenAI’s announcement gives the current details.
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Text detectors can look for a supported watermark or estimate whether prose resembles AI output. Style alone is weak evidence: polished, formulaic, or unusually even writing does not prove a machine wrote it, and human editing can change a text’s apparent patterns.
Why a detector score is not proof
Detection depends on the system, passage length, content, and edits. In OpenAI’s 2026 evaluation of its text watermarking system on psychology passages, detection was about 80% for 200-token passages and about 95% for 400-token passages at a target false-positive rate of 1%. These are results for that system and evaluation—not general accuracy rates for AI detectors.
In the same evaluation, detection for 400-token passages fell from about 92% to 66% after replacing 10% of words with synonyms, and to 17% after replacing 25%. This illustrates how editing can weaken a watermark; it does not predict every detector’s performance on every text. OpenAI also says a watermark cannot quantify human judgment, editing, or creativity.
More broadly, the European Commission’s 2026 technical report groups text-provenance approaches into watermarking, structural marking, metadata, logging, and AI-generated-text detection. It assesses methods using dimensions including effectiveness, robustness, reliability, accessibility, and interoperability. The categories have different tradeoffs; none answers every provenance question on its own. The Commission report describes the framework.
A practical way to assess a suspicious post, image, or recording
- Preserve the original. Keep the original file and any available context, such as where and when it first appeared. Avoid transformations before a provenance check.
- Trace its earliest available source. Look for an original publication, creator statement, or process history rather than relying on a repost or screenshot.
- Use a supported check, if one exists. Confirm which provider, format, and signal the tool covers. Treat a positive result as evidence of that signal, not a verdict on authorship or truth.
- Corroborate independently. Check factual claims against verifiable records, original sources, or independent reporting. Provenance and accuracy are separate questions.
- Describe the result precisely. Say “a supported signal was detected” or “no supported signal was found.” Do not turn either result into “AI-generated” or “human-made” without other evidence.
- Use extra care before making an accusation. Treat detector output as a lead, seek independent evidence, and give the creator an opportunity to explain the content’s provenance or editing history. This is prudent editorial practice, not a legal standard.
When do disclosure rules apply?
Disclosure requirements depend on jurisdiction and use; they are not a universal rule for every AI-assisted sentence. The European Commission says Article 50 of the EU AI Act applies from August 2, 2026, with specified marking and disclosure obligations. Its examples include deepfakes and public-interest text published without human review or editorial control. Whether a particular work is covered depends on the applicable obligation and circumstances. The Commission’s transparency guidelines summarize the obligations.
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